Utilization of defined microbial communities enables effective evaluation of meta-genomic assemblies

Abstract

BackgroundMetagenomics is the study of the microbial genomes isolated from communities found on our bodies or in our environment. By correctly determining the relation between human health and the human associated microbial communities, novel mechanisms of health and disease can be found, thus enabling the development of novel diagnostics and therapeutics. Due to the diversity of the microbial communities, strategies developed for aligning human genomes cannot be utilized, and genomes of the microbial species in the community must be assembled de novo. However, in order to obtain the best metagenomic assemblies, it is important to choose the proper assembler. Due to the rapidly evolving nature of metagenomics, new assemblers are constantly created, and the field has not yet agreed on a standardized process. Furthermore, the truth sets used to compare these methods are either too simple (computationally derived diverse communities) or complex (microbial communities of unknown composition), yielding results that are hard to interpret. In this analysis, we interrogate the strengths and weaknesses of five popular assemblers through the use of defined biological samples of known genomic composition and abundance. We assessed the performance of each assembler on their ability to reassemble genomes, call taxonomic abundances, and recreate open reading frames (ORFs).ResultsWe tested five metagenomic assemblers: Omega, metaSPAdes, IDBA-UD, metaVelvet and MEGAHIT on known and synthetic metagenomic data sets. MetaSPAdes excelled in diverse sets, IDBA-UD performed well all around, metaVelvet had high accuracy in high abundance organisms, and MEGAHIT was able to accurately differentiate similar organisms within a community. At the ORF level, metaSPAdes and MEGAHIT had the least number of missing ORFs within diverse and similar communities respectively.ConclusionsDepending on the metagenomics question asked, the correct assembler for the task at hand will differ. It is important to choose the appropriate assembler, and thus clearly define the biological problem of an experiment, as different assemblers will give different answers to the same question.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3679-5) contains supplementary material, which is available to authorized users.

Evaluating Metagenome Assembly on a Simple Defined Community with Many Strain Variants

“…In general other studies have found that MEGAHIT excels in recovery of sequence through assembly [3, 16] and is considerably more computationally efficient than most other assemblers [3, 15]. However, studies have also shown that MEGAHIT produces more misassemblies than other assemblers [3] and performs poorly on high coverage portions of the data set [5] Thus while we can recommend MEGAHIT as a good first assembler, we can also not unambiguously recommend it as the only assembler to use.…”

“…[5] explored metagenome assembler performance on a pair of real data sets, again concluding that the biological goal and computational resources defined the choice of assembler. Also see [16] for an analysis of a previously generated HMP benchmark data set; however, the Illumina reads used for this study are much shorter than current sequencing and are arguably not relevant for future studies.…”

Accurate and complete genomes from metagenomes

“…For example, although Prochlorococcus and SAR11 are among the most abundant bacteria in ocean habitats, the co-occurrence of closely related strains (Giovannoni 2017) leads to very fragmented MAGs and poor representation in the final data sets (Delmont et al 2018;Tully et al 2018). Of the three commonly used metagenomic assemblers, IDBA-UD, MEGAHIT, and metaSPAdes (Greenwald et al 2017), metaSPAdes was best designed to handle microvariations between fragments from related strains to generate longer composite sequences (Olson et al 2017). However, care should be taken when undertaking detailed analyses (e.g., biochemical testing) of open reading frames generated in this way because they may be chimeric.…”

Accurate and Complete Genomes from Metagenomes

“…For example, although Prochlorococcus and SAR11 are among the most abundant bacteria in ocean habitats, the co-occurence of closely related strains (Giovannoni 2017) leads to very fragmented MAGs and poor representation in the final datasets (Delmont et al 2018;Tully et al 2018) . Of the three commonly used metagenomic assemblers, IDBA_UD, MEGAHIT, metaSPAdes (Greenwald et al 2017) , metaSPAdes was best designed to handle micro-variations between fragments from related strains to generate longer composite sequences (Olson et al 2017) . However, care should be taken when undertaking detailed analyses (e.g., biochemical testing) of open reading frames generated in this way as they may be chimeric.…”